OpenRaiser/Intern-Atlas huggingface dataset:
Data Protection by Design for Cybersecurity Systems in a Smart Home Environment
Venue: arXiv cs.CR 2019
Year: 2019

The present paper deals with the elucidation and implementation of the Data Protection by Design (DPbD) principle as recently introduced in the European Union data protection law, specifically with regards to cybersecurity systems in a Smart Home environment, both from a legal and a technical perspective. Starting point constitutes the research conducted in the Cyber-Trust project, which endeavours the development of an innovative and customisable cybersecurity platform for cyber-threat intelligence gathering, detection and mitigation within the Internet of Things ecosystem. During the course of the paper, the requirements of DPbD with regards to the conceptualisation, design and actual development of the system are presented as prescribed in law. These requirements are then translated into technical solutions, as envisaged in the Cyber-Trust system. For trade-offs are not foreign to the DPbD context, technical limitations and legal challenges are also discussed in this interdisciplinary dialogue.
Soft Robots for Extreme Environments: Removing Electronic Control
Venue: arXiv cs.RO 2019
Year: 2019

The ignition of flammable liquids and gases in offshore oil and gas environments is a major risk and can cause loss of life, serious injury, and significant damage to infrastructure. Power supplies that are used to provide regulated voltages to drive motors, relays, and power electronic controls can produce heat and cause sparks. As a result, the European Union requires ATEX certification on electrical equipment to ensure safety in such extreme environments. Implementing designs that meet this standard is time-consuming and adds to the cost of operations. Soft robots are often made with soft materials and can be actuated pneumatically, without electronics, making these systems inherently compliant with this directive. In this paper, we aim to increase the capability of new soft robotic systems moving from a one-to-one control-actuator architecture and implementing an electronics-free control system. We have developed a robot that demonstrates locomotion and gripping using three-pneumatic lines: a vacuum power line, a control input, and a clock line. We have followed the design principles of digital electronics and demonstrated an integrated fluidic circuit with eleven, fully integrated fluidic switches and six actuators. We have realized the basic building blocks of logical operation into combinational logic and memory using our fluidic switches to create a two-state automata machine. This system expands on the state of the art increasing the complexity over existing soft systems with integrated control.
RecSys-DAN: Discriminative Adversarial Networks for Cross-Domain Recommender Systems
Venue: arXiv cs.IR 2019
Year: 2019

Data sparsity and data imbalance are practical and challenging issues in cross-domain recommender systems. This paper addresses those problems by leveraging the concepts which derive from representation learning, adversarial learning and transfer learning (particularly, domain adaptation). Although various transfer learning methods have shown promising performance in this context, our proposed novel method RecSys-DAN focuses on alleviating the cross-domain and within-domain data sparsity and data imbalance and learns transferable latent representations for users, items and their interactions. Different from existing approaches, the proposed method transfers the latent representations from a source domain to a target domain in an adversarial way. The mapping functions in the target domain are learned by playing a min-max game with an adversarial loss, aiming to generate domain indistinguishable representations for a discriminator. Four neural architectural instances of ResSys-DAN are proposed and explored. Empirical results on real-world Amazon data show that, even without using labeled data (i.e., ratings) in the target domain, RecSys-DAN achieves competitive performance as compared to the state-of-the-art supervised methods. More importantly, RecSys-DAN is highly flexible to both unimodal and multimodal scenarios, and thus it is more robust to the cold-start recommendation which is difficult for previous methods.
Algorithms and Improved bounds for online learning under finite hypothesis class
Venue: arXiv cs.LG 2019
Year: 2019

Online learning is the process of answering a sequence of questions based on the correct answers to the previous questions. It is studied in many research areas such as game theory, information theory and machine learning. There are two main components of online learning framework. First, the learning algorithm also known as the learner and second, the hypothesis class which is essentially a set of functions which learner uses to predict answers to the questions. Sometimes, this class contains some functions which have the capability to provide correct answers to the entire sequence of questions. This case is called realizable case. And when hypothesis class does not contain such functions is called unrealizable case. The goal of the learner, in both the cases, is to make as few mistakes as that could have been made by most powerful functions in hypothesis class over the entire sequence of questions. Performance of the learners is analysed by theoretical bounds on the number of mistakes made by them. This paper proposes three algorithms to improve the mistakes bound in the unrealizable case. Proposed algorithms perform highly better than the existing ones in the long run when most of the input sequences presented to the learner are likely to be realizable.
Trends, Challenges and Adopted Strategies in RoboCup@Home (2019 version)
Venue: arXiv cs.RO 2019
Year: 2019

Scientific competitions are crucial in the field of service robotics. They foster knowledge exchange and benchmarking, allowing teams to test their research in unstandardized scenarios. In this paper, we summarize the trending solutions and approaches used in RoboCup@Home. Further on, we discuss the attained achievements and challenges to overcome in relation with the progress required to fulfill the long-term goal of the league. Consequently, we propose a set of milestones for upcoming competitions by considering the current capabilities of the robots and their limitations. With this work we aim at laying the foundations towards the creation of roadmaps that can help to direct efforts in testing and benchmarking in robotics competitions.
A New Approach for Semi-automatic Building and Extending a Multilingual Terminology Thesaurus
Venue: arXiv cs.CL 2019
Year: 2019

This paper describes a new system for semi-automatically building, extending and managing a terminological thesaurus---a multilingual terminology dictionary enriched with relationships between the terms themselves to form a thesaurus. The system allows to radically enhance the workflow of current terminology expert groups, where most of the editing decisions still come from introspection. The presented system supplements the lexicographic process with natural language processing techniques, which are seamlessly integrated to the thesaurus editing environment. The system's methodology and the resulting thesaurus are closely connected to new domain corpora in the six languages involved. They are used for term usage examples as well as for the automatic extraction of new candidate terms. The terminological thesaurus is now accessible via a web-based application, which a) presents rich detailed information on each term, b) visualizes term relations, and c) displays real-life usage examples of the term in the domain-related documents and in the context-based similar terms. Furthermore, the specialized corpora are used to detect candidate translations of terms from the central language (Czech) to the other languages (English, French, German, Russian and Slovak) as well as to detect broader Czech terms, which help to place new terms in the actual thesaurus hierarchy. This project has been realized as a terminological thesaurus of land surveying, but the presented tools and methodology are reusable for other terminology domains.
Ground Profile Recovery from Aerial 3D LiDAR-based Maps
Venue: arXiv cs.RO 2019
Year: 2019

The paper presents the study and implementation of the ground detection methodology with filtration and removal of forest points from LiDAR-based 3D point cloud using the Cloth Simulation Filtering (CSF) algorithm. The methodology allows to recover a terrestrial relief and create a landscape map of a forestry region. As the proof-of-concept, we provided the outdoor flight experiment, launching a hexacopter under a mixed forestry region with sharp ground changes nearby Innopolis city (Russia), which demonstrated the encouraging results for both ground detection and methodology robustness.
Privacy-preserving Active Learning on Sensitive Data for User Intent Classification
Venue: arXiv cs.LG 2019
Year: 2019

Active learning holds promise of significantly reducing data annotation costs while maintaining reasonable model performance. However, it requires sending data to annotators for labeling. This presents a possible privacy leak when the training set includes sensitive user data. In this paper, we describe an approach for carrying out privacy preserving active learning with quantifiable guarantees. We evaluate our approach by showing the tradeoff between privacy, utility and annotation budget on a binary classification task in a active learning setting.
An Intuitive Derivation of the Coherence Index Relation in Compressive Sensing
Venue: arXiv cs.IT 2019
Year: 2019

The existence and uniqueness conditions are a prerequisite for reliable reconstruction of sparse signals from reduced sets of measurements within the Compressive Sensing (CS) paradigm. However, despite their underpinning role for practical applications, existing uniqueness relations are either computationally prohibitive to implement (Restricted Isometry Property), or involve mathematical tools that are beyond the standard background of engineering graduates (Coherence Index). This can introduce conceptual and computational obstacles in the development of engineering intuition, the design of suboptimal practical solutions, or understanding of limitations. To this end, we introduce a simple but rigorous derivation of the coherence index condition, based on standard linear algebra, with the aim to empower signal processing practitioners with intuition in the design and ease in implementation of CS systems. Given that the coherence index is one of very few CS metrics that admits mathematically tractable and computationally feasible calculation, it is our hope that this work will help bridge the gap between the theory and applications of compressive sensing.
Harnessing Intrinsic Noise in Memristor Hopfield Neural Networks for Combinatorial Optimization
Venue: arXiv cs.ET 2019
Year: 2019

We describe a hybrid analog-digital computing approach to solve important combinatorial optimization problems that leverages memristors (two-terminal nonvolatile memories). While previous memristor accelerators have had to minimize analog noise effects, we show that our optimization solver harnesses such noise as a computing resource. Here we describe a memristor-Hopfield Neural Network (mem-HNN) with massively parallel operations performed in a dense crossbar array. We provide experimental demonstrations solving NP-hard max-cut problems directly in analog crossbar arrays, and supplement this with experimentally-grounded simulations to explore scalability with problem size, providing the success probabilities, time and energy to solution, and interactions with intrinsic analog noise. Compared to fully digital approaches, and present-day quantum and optical accelerators, we forecast the mem-HNN to have over four orders of magnitude higher solution throughput per power consumption. This suggests substantially improved performance and scalability compared to current quantum annealing approaches, while operating at room temperature and taking advantage of existing CMOS technology augmented with emerging analog non-volatile memristors.
Grammatical Error Correction and Style Transfer via Zero-shot Monolingual Translation
Venue: arXiv cs.CL 2019
Year: 2019

Both grammatical error correction and text style transfer can be viewed as monolingual sequence-to-sequence transformation tasks, but the scarcity of directly annotated data for either task makes them unfeasible for most languages. We present an approach that does both tasks within the same trained model, and only uses regular language parallel data, without requiring error-corrected or style-adapted texts. We apply our model to three languages and present a thorough evaluation on both tasks, showing that the model is reliable for a number of error types and style transfer aspects.
Deformable kernel networks for guided depth map upsampling
Venue: arXiv cs.CV 2019
Year: 2019

We address the problem of upsampling a low-resolution (LR) depth map using a registered high-resolution (HR) color image of the same scene. Previous methods based on convolutional neural networks (CNNs) combine nonlinear activations of spatially-invariant kernels to estimate structural details from LR depth and HR color images, and regress upsampling results directly from the networks. In this paper, we revisit the weighted averaging process that has been widely used to transfer structural details from hand-crafted visual features to LR depth maps. We instead learn explicitly sparse and spatially-variant kernels for this task. To this end, we propose a CNN architecture and its efficient implementation, called the deformable kernel network (DKN), that outputs sparse sets of neighbors and the corresponding weights adaptively for each pixel. We also propose a fast version of DKN (FDKN) that runs about 17 times faster (0.01 seconds for a HR image of size 640 x 480). Experimental results on standard benchmarks demonstrate the effectiveness of our approach. In particular, we show that the weighted averaging process with 3 x 3 kernels (i.e., aggregating 9 samples sparsely chosen) outperforms the state of the art by a significant margin.
Image search using multilingual texts: a cross-modal learning approach between image and text
Venue: arXiv cs.CV 2019
Year: 2019

Multilingual (or cross-lingual) embeddings represent several languages in a unique vector space. Using a common embedding space enables for a shared semantic between words from different languages. In this paper, we propose to embed images and texts into a unique distributional vector space, enabling to search images by using text queries expressing information needs related to the (visual) content of images, as well as using image similarity. Our framework forces the representation of an image to be similar to the representation of the text that describes it. Moreover, by using multilingual embeddings we ensure that words from two different languages have close descriptors and thus are attached to similar images. We provide experimental evidence of the efficiency of our approach by experimenting it on two datasets: Common Objects in COntext (COCO) [19] and Multi30K [7].
Multilevel Text Normalization with Sequence-to-Sequence Networks and Multisource Learning
Venue: arXiv cs.CL 2019
Year: 2019

We define multilevel text normalization as sequence-to-sequence processing that transforms naturally noisy text into a sequence of normalized units of meaning (morphemes) in three steps: 1) writing normalization, 2) lemmatization, 3) canonical segmentation. These steps are traditionally considered separate NLP tasks, with diverse solutions, evaluation schemes and data sources. We exploit the fact that all these tasks involve sub-word sequence-to-sequence transformation to propose a systematic solution for all of them using neural encoder-decoder technology. The specific challenge that we tackle in this paper is integrating the traditional know-how on separate tasks into the neural sequence-to-sequence framework to improve the state of the art. We address this challenge by enriching the general framework with mechanisms that allow processing the information on multiple levels of text organization (characters, morphemes, words, sentences) in combination with structural information (multilevel language model, part-of-speech) and heterogeneous sources (text, dictionaries). We show that our solution consistently improves on the current methods in all three steps. In addition, we analyze the performance of our system to show the specific contribution of the integrating components to the overall improvement.
Non-recursive equivalent of the conjugate gradient method without the need to restart
Venue: arXiv cs.NA 2019
Year: 2019

A simple alternative to the conjugate gradient(CG) method is presented; this method is developed as a special case of the more general iterated Ritz method (IRM) for solving a system of linear equations. This novel algorithm is not based on conjugacy, i.e. it is not necessary to maintain overall orthogonalities between various vectors from distant steps. This method is more stable than CG, and restarting techniques are not required. As in CG, only one matrix-vector multiplication is required per step with appropriate transformations. The algorithm is easily explained by energy considerations without appealing to the A-orthogonality in n-dimensional space. Finally, relaxation factor and preconditioning-like techniques can be adopted easily.
Smoothed finite element method for stress intensity factor estimation: benefits and limitations
Venue: arXiv cs.CE 2019
Year: 2019

The aim of this study was to check how efficient can be smoothed finite element method (FEM) for solution of the linear fracture mechanics problems. Accuracy of stress intensity factor (SIF) computation were investigated using three types of smoothed FEM (namely ES-FEM, NS-FEM and $α$-FEM). Two types of simple benchmark problems (uniaxial tension of the specimens with central and edge cracks) have been considered. SIF values were calculated by virtual crack extension and modified crack closure integral methods on almost uniform meshes. Influence of the mesh density and type of the method used for SIF determination were investigated too. Conclusions: 1) $α$-FEM has the highest accuracy among all tested methods. 2) In the case of the simplest linear triangles, ES-FEM accuracy is same as in the case of traditional FEM when second order (quadratic) elements are used. 3) NS-FEM always overestimates SIF values. Unfortunately, solutions obtained using this method or $α$-FEM contain unphysical oscillations of displacements and stress near the crack tip.
Batched Sparse Matrix Multiplication for Accelerating Graph Convolutional Networks
Venue: arXiv cs.DC 2019
Year: 2019

Graph Convolutional Networks (GCNs) are recently getting much attention in bioinformatics and chemoinformatics as a state-of-the-art machine learning approach with high accuracy. GCNs process convolutional operations along with graph structures, and GPUs are used to process enormous operations including sparse-dense matrix multiplication (SpMM) when the graph structure is expressed as an adjacency matrix with sparse matrix format. However, the SpMM operation on small graph, where the number of nodes is tens or hundreds, hardly exploits high parallelism or compute power of GPU. Therefore, SpMM becomes a bottleneck of training and inference in GCNs applications. In order to improve the performance of GCNs applications, we propose new SpMM algorithm especially for small sparse matrix and Batched SpMM, which exploits high parallelism of GPU by processing multiple SpMM operations with single CUDA kernel. To the best of our knowledge, this is the first work of batched approach for SpMM. We evaluated the performance of the GCNs application on TSUBAME3.0 implementing NVIDIA Tesla P100 GPU, and our batched approach shows significant speedups of up to 1.59x and 1.37x in training and inference, respectively.
Estimation and Verification of Partially-Observed Discrete-Event Systems
Venue: arXiv cs.SY 2019
Year: 2019

This article considers state estimation and veri cation problems for an important class of man-made cyber-physical systems called Discrete-Event Systems (DES).
REsCUE: A framework for REal-time feedback on behavioral CUEs using multimodal anomaly detection
Venue: arXiv cs.HC 2019
Year: 2019

Executive coaching has been drawing more and more attention for developing corporate managers. While conversing with managers, coach practitioners are also required to understand internal states of coachees through objective observations. In this paper, we present REsCUE, an automated system to aid coach practitioners in detecting unconscious behaviors of their clients. Using an unsupervised anomaly detection algorithm applied to multimodal behavior data such as the subject's posture and gaze, REsCUE notifies behavioral cues for coaches via intuitive and interpretive feedback in real-time. Our evaluation with actual coaching scenes confirms that REsCUE provides the informative cues to understand internal states of coachees. Since REsCUE is based on the unsupervised method and does not assume any prior knowledge, further applications beside executive coaching are conceivable using our framework.
Effect of Values and Technology Use on Exercise: Implications for Personalized Behavior Change Interventions
Venue: arXiv cs.HC 2019
Year: 2019

Technology has recently been recruited in the war against the ongoing obesity crisis; however, the adoption of Health & Fitness applications for regular exercise is a struggle. In this study, we present a unique demographically representative dataset of 15k US residents that combines technology use logs with surveys on moral views, human values, and emotional contagion. Combining these data, we provide a holistic view of individuals to model their physical exercise behavior. First, we show which values determine the adoption of Health & Fitness mobile applications, finding that users who prioritize the value of purity and de-emphasize values of conformity, hedonism, and security are more likely to use such apps. Further, we achieve a weighted AUROC of .673 in predicting whether individual exercises, and we also show that the application usage data allows for substantially better classification performance (.608) compared to using basic demographics (.513) or internet browsing data (.546). We also find a strong link of exercise to respondent socioeconomic status, as well as the value of happiness. Using these insights, we propose actionable design guidelines for persuasive technologies targeting health behavior modification.
Characteristic-Dependent Linear Rank Inequalities via Complementary Vector Spaces
Venue: arXiv cs.IT 2019
Year: 2019

A characteristic-dependent linear rank inequality is a linear inequality that holds by ranks of subspaces of a vector space over a finite field of determined characteristic, and does not in general hold over other characteristics. In this paper, we produce new characteristic-dependent linear rank inequalities by an alternative technique to the usual Dougherty's inverse function method [9]. We take up some ideas of Blasiak [4], applied to certain complementary vector spaces, in order to produce them. Also, we present some applications to network coding. In particular, for each finite or co-finite set of primes $P$, we show that there exists a sequence of networks $\mathcal{N}\left(k\right)$ in which each member is linearly solvable over a field if and only if the characteristic of the field is in $P$, and the linear capacity, over fields whose characteristic is not in $P$, $\rightarrow0$ as $k\rightarrow\infty$.